Advances in Quantum System Design Strategies

I am pleased to highlight some recent work from the team that further evolves our understanding of building practical quantum computing architectures with bivariate bicycle codes and that addresses one of the fundamental challenges to real-time decoding. Our Nature paper from 2024 [https://lnkd.in/eS26sKx6] showed that a quantum memory using bivariate bicycle codes requires roughly 10x fewer physical qubits compared to the surface code. An important question to answer was whether this advantage is retained not only while storing information in memory but also during computations. To answer that question, our team designed fault-tolerant logical instruction sets for the codes and developed a strategy to compile circuits to these instructions. Using these tools, they performed end-to-end resource estimates demonstrating that bicycle architectures retain an order of magnitude qubit advantage over surface code architectures when implementing large logical circuits. The pre-print can be found here [https://lnkd.in/e7k7gYs7] One of the central doubts about the practicality of quantum low-density parity check (qLDPC) codes such as the bivariate bicycle codes has been the difficulty of real-time decoding. The second preprint [https://lnkd.in/eFbWNFeU] we posted this week hopefully puts those doubts to rest. A large challenge in decoding qLDPC codes arises from the perceived need for two-stage decoding solutions such as belief propagation (BP) followed by ordered statistics decoding (OSD). In particular, real-time implementation of OSD appears very challenging, which has spawned efforts to reduce the cost of OSD. Our team took a different approach. This new result shows that one can eliminate the need for a second-stage decoder altogether through a suitable modification of the BP algorithm. Our modified algorithm, called Relay-BP, enhances the traditional method by incorporating spatially disordered memory terms. This dampens oscillations and breaks symmetries that trap traditional BP algorithms. The result is an algorithm that outperforms the current state-of-the-art approach while simultaneously still being amenable to implementation in an FPGA. Congratulations to the team for these exciting advancements, which validate our strategy and move us one step closer to realizing a fault-tolerant quantum system.

Headline: Silicon Quantum Breakthrough Enables Full Logical Operations with Built-In Error Detection Introduction Researchers at the Shenzhen International Quantum Academy have achieved a critical milestone by demonstrating a silicon-based quantum processor capable of full logical operations with error detection. This marks a significant step toward practical, scalable quantum computing using materials already foundational to modern electronics. Key Developments and Breakdown First-of-Its-Kind Silicon Achievement Demonstrated a silicon quantum processor performing a complete set of logical operations. Integrates error detection directly into computation, a requirement for fault-tolerant systems. Previously, similar capabilities were largely limited to superconducting platforms. Logical Qubit Implementation Used four physical qubits to encode two logical qubits capable of detecting errors. Logical qubits provide resilience against noise, one of quantum computing’s core challenges. System successfully identified and flagged interference during computation. Advanced Fabrication Techniques Built using phosphorus atoms precisely embedded in silicon. Achieved atomic-level control over qubit placement and behavior. Introduced methods to reduce signal interference, improving system reliability. Bridging to Scalable Hardware Silicon compatibility aligns quantum development with existing semiconductor infrastructure. Enhances potential for mass manufacturing and integration into current chip ecosystems. Demonstrates that key building blocks for fault-tolerant quantum computing are achievable in silicon. Why It Matters and Broader Implications This breakthrough positions silicon as a viable platform for scalable quantum computing, bridging the gap between experimental systems and industrial deployment. By combining logical operations with real-time error detection, the research validates a core requirement for reliable quantum machines. For industry and governments, the implication is profound: quantum computing may evolve within the existing semiconductor paradigm rather than requiring entirely new infrastructure. This convergence accelerates commercialization timelines and strengthens the strategic importance of quantum readiness across technology, defense, and economic sectors. I share daily insights with tens of thousands followers across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

QUANTUM COMPUTERS RECYCLE QUBITS TO MINIMAZE ERRORS AND ENHANCE COMPUTATIONAL EFFICIENCY Quantum computing represents a paradigm shift in information processing, with the potential to address computationally intractable problems beyond the scope of classical architectures. Despite significant advances in qubit design and hardware engineering, the field remains constrained by the intrinsic fragility of quantum states. Qubits are highly susceptible to decoherence, environmental noise, and control imperfections, leading to error propagation that undermines large‑scale reliability. Recent research has introduced qubit recycling as a novel strategy to mitigate these limitations. Recycling involves the dynamic reinitialization of qubits during computation, restoring them to a well‑defined ground state for subsequent reuse. This approach reduces the number of physical qubits required for complex algorithms, limits cumulative error rates, and increases computational density. Particularly, Atom Computing’s AC1000 employs neutral atoms cooled to near absolute zero and confined in optical lattices. These cold atom qubits exhibit extended coherence times and high atomic uniformity, properties that make them particularly suitable for scalable architectures. The AC1000 integrates precision optical control systems capable of identifying qubits that have degraded and resetting them mid‑computation. This capability distinguishes it from conventional platforms, which often require qubits to remain pristine or be discarded after use. From an engineering perspective, minimizing errors and enhancing computational efficiency requires a multi‑layered strategy. At the hardware level, platforms such as cold atoms, trapped ions, and superconducting circuits are being refined to extend coherence times, reduce variability, and isolate quantum states from environmental disturbances. Dynamic qubit management adds resilience, with recycling and active reset protocols restoring qubits mid‑computation, while adaptive scheduling allocates qubits based on fidelity to optimize throughput. Error‑correction frameworks remain central, combining redundancy with recycling to reduce overhead and enable fault‑tolerant architectures. Algorithmic and architectural efficiency further strengthens performance through optimized gate sequences, hybrid classical–quantum workflows, and parallelization across qubit clusters. Looking ahead, metamaterials innovation, machine learning‑driven error mitigation, and modular metasurface architectures promise to accelerate progress toward scalable systems. The implications of qubit recycling and these complementary strategies are substantial. By enabling more complex computations with fewer physical resources, they can reduce hardware overhead and enhance reliability. This has direct relevance for domains such as cryptography, materials discovery, pharmaceutical design, and large‑scale optimization.

The best part of being a Deeptech VC is how it feeds your curiosity. Arguably one of the most fundamental skills in venture capital. The bleeding edge of research is where I like meandering to anticipate possible commercialisation trends. Quantum computing is one of these areas. I've already made a couple of bets here, and the field continues to advance through both commercial milestones and research breakthroughs. Hybrid systems for chemistry computation are a particularly interesting field to immerse yourself on a Sunday 🤓☕ Take this recently published research: Korean researchers just achieved something remarkable with photonic qudits - think of them as quantum Lego blocks that can hold multiple states instead of just 0s and 1s. While Google and IBM struggle with 12-qubit systems requiring complex error corrections on superconducting architectures, this team achieved 16-dimensional calculations using a single quantum unit by using photonics. The numbers are striking: • Chemical accuracy of 0.00146 Hartree for H2 molecules • No error correction needed • 48 iterations vs traditional systems' hundreds • 5x faster convergence than previous approaches Think of it as the difference between building with individual blocks (qubits) versus using pre-fabricated sections (qudits). Why this matters for investors: 1. Resource efficiency = lower operational costs 2. Scalability without the error cascade nightmare 3. Room temperature operation = practical deployment The quantum-AI race in chemistry is finally heating up 🏎️🏁 • AI can handle 100k atoms but struggles with quantum precision • Quantum systems nail accuracy but only for smaller molecules • Hybrid approaches could bridge this gap This is where the next wave of quantum products may emerge - at the intersection of classical AI and quantum advantages. For LPs, angels and VCs looking at quantum in their portfolios: watch the hybrid plays. AI alone may be too resource intensive to advance much further in these areas. Quantum solutions are just getting started. The future isn't necessarily binary anymore - it may be hybrid. Just like these qudits. #QuantumComputing #DeepTech #VentureCapital #FutureOfComputing #AI Thoughts? 🤔

Dear Prof Feynman, Since your 1982 paper “Simulating Physics with Computers,” quantum computing has developed from speculation into experimental reality. Here’s where we stand in June 2025. Your insight that classical computers cannot efficiently simulate quantum systems proved correct - this became the foundation for building quantum computers. Ion trapping techniques developed in the 1980s now control dozens of trapped ions as quantum bits, enabling high accuracy in single quantum operations and extended coherence times. Josephson junctions became artificial atoms: superconducting circuits that manipulate quantum states at millikelvin temperatures. Current superconducting processors include Google’s Willow chip and IBM’s advanced systems. Two-qubit gate accuracies approach 99%, though environmental noise still limits algorithmic applications to dozens of useful qubits working together. Shor’s factoring algorithm works on small numbers but would need millions of error-corrected quantum bits for practical cryptography. Google’s 2019 quantum demonstration solved a sampling problem faster than classical computers, though the practical advantage is close to nil. Scientists have built logical quantum bits that actually last longer and make fewer errors than the physical quantum bits they’re made from. However, fault-tolerant computation requires significant overhead, necessitating many physical quantum bits per logical quantum bit. IBM plans to develop 200-logical-qubit systems by 2029, utilizing advanced error correction codes. Your original challenge persists. Quantum many-body systems remain exponentially hard to simulate classically, yet building quantum simulators requires controlling thousands of quantum components with extraordinary precision.

The first time I saw machine learning in action for quantum computing was during my time at the Niels Bohr Institute, University of Copenhagen. Anasua Chatterjee and colleagues were exploring AI-driven methods to automate the tune-up of spin qubits. To be honest, I didn’t give it much attention at the time. Fast forward to today, and AI feels like the secret sauce accelerating almost every aspect of quantum computing. Think about it: quantum computing is all about mastering exponentially complex systems. AI thrives in high-dimensional, data-rich environments. This pairing? It’s like finding the perfect dance partner. Here’s what’s exciting: AI isn’t just helping to debug or optimize—it’s diving deep into the heart of quantum research. It’s designing qubits, discovering novel error correction codes, and making circuit synthesis more efficient than ever. Tasks that once took teams of researchers weeks to figure out are now becoming automated, adaptive, and scalable. One example I really like? AI-enhanced quantum error correction. Researchers are using neural networks and transformers to achieve error rates below what traditional methods can manage—and they’re doing it at a fraction of the computational cost. Another idea that’s caught my attention is quantum feedback control using transformers. This approach could change how we stabilize and steer quantum systems in real time by leveraging AI models to predict and counteract noise. The question now is: how long before we see more of these theoretical breakthroughs transition to real hardware? Natalia Ares, is quantum feedback control with transformers already in the works? This is such an exciting direction for quantum control and AI! 📸 Credits: Yuri Alexeev et al. (2024)

🚀 Excited to share that my latest paper “Quantum AI: Harnessing the Power of Quantum Computing for Scalable and Adaptive Learning” has now been officially published in the proceedings of IEEE International On-Line Test Symposium (IOLTS) 2025 🎉 In this work, I present a unified framework for building scalable and adaptive Quantum AI systems, with a focus on: 1. Quantum Long Short-Term Memory (QLSTM) for sequential learning 2. Quantum Federated Learning (QFL) for privacy-preserving distributed intelligence 3. Quantum Reinforcement Learning (QRL) for dynamic decision-making 4. Quantum Fast Weight Programmer (QFWP) for meta-learning and rapid adaptation 5. Differentiable Quantum Architecture Search (DiffQAS) for automated circuit design Despite challenges such as noise, decoherence, and limited qubits, this paper outlines strategies—hybrid training, error-aware optimization, and scalable architectures—that push us toward trustworthy, generalizable, and future-ready Quantum AI. I’m grateful for the opportunity to contribute to IEEE IOLTS and the broader quantum computing community. Looking forward to continuing this journey toward making Quantum AI a practical reality. 🌌✨ 📄 Read the paper here: https://lnkd.in/eNMnVcjt You can get the full text also here: https://lnkd.in/e5HKx-qH #QuantumAI #MachineLearning #ReinforcementLearning #FederatedLearning #QuantumComputing #IOLTS2025

The quantum landscape is shifting faster than most people realize. In the last 72 hours alone, we’ve seen three signals that define where the next decade is heading: 1. Industrial quantum manufacturing is no longer theoretical. Companies capable of building repeatable, export‑ready quantum systems at scale are separating from the pack. The shift from prototype culture to manufacturing culture is now the real competitive frontier. 2. Frontier materials science just broke a thermal barrier. University of Southern California ’s new 1300°F (700°C) memristor demonstrates that computation can survive and compute in environments where silicon dies instantly. That unlocks AI and quantum‑adjacent systems for aerospace, geothermal, fusion, and defense applications previously considered impossible. 3. Quantum materials are beginning to harvest energy from the environment. The nonlinear Hall effect (NLHE) work from QUT/NTU shows that imperfections and lattice vibrations can be engineered to convert ambient AC signals directly into DC power. Imagine sensors, chips, and edge devices operating without batteries powered by the quantum behavior of the material itself.These aren’t isolated breakthroughs. They’re converging.Quantum is becoming an industrial ecosystem spanning manufacturing, materials, energy, and computation.And the organizations that understand how these pieces fit together will define the next era of infrastructure.For teams navigating this transition from national programs to enterprise R&D I help map these signals into strategy: manufacturing readiness, substrate alignment, deployment pathways, and cross‑ecosystem positioning.The next decade belongs to the builders who can see the whole board.🖤🔥 #QuantumComputing #QuantumHardware #DeepTech #QuantumMaterials #IndustrialQuantum #AIInfrastructure #NextGenElectronics #QuantumEcosystem

Exciting quantum computing progress from #ColumbiaUniversity’s Quantum Initiative! Professors Sebastian Will (Physics) and Nanfang Yu (Applied Physics & Applied Mathematics) are pioneering a powerful approach to large-scale quantum systems using neutral-atom arrays. In their latest work, the team combined optical tweezers with engineered metasurfaces to trap over 1,000 strontium atoms, and they see a clear path toward 100,000+ qubits—a scale that could dramatically advance quantum computing performance. Unlike many other qubit platforms, neutral atoms are identical by nature, simplifying control and scaling. Key innovations: • Novel metasurface-based optical tweezers for massively scalable atom arrays • Successfully trapping and controlling more than 1,000 atoms • A scalable foundation for high-qubit quantum computing platforms Congratulations to Prof. Will, Prof. Yu, and their teams for this impactful step toward truly large-scale quantum hardware! Their work not only pushes fundamental science but also brings us closer to quantum systems capable of solving complex simulations and optimization challenges that classical computers cannot. https://lnkd.in/eVSV8GbN #QuantumComputing #NeutralAtoms #Metasurfaces #Qubits #ColumbiaResearch #OpticalTweezers #Innovation #TechLeadership #ColumbiaEngineering

A new quantum machine learning technique based on graph neural networks to produce QPU-aware quantum kernels can be an interesting path to explore. A recent paper, "Hardware-Aware Quantum Kernel Design Based on Graph Neural Networks," introduces an innovative framework called HaQGNN. This work addresses a critical challenge in quantum machine learning: designing effective quantum kernels that can adapt to both specific tasks and the limitations of near-term quantum hardware. HaQGNN could offer several key advantages for advancing practical quantum kernel design on NISQ devices: * Hardware awareness: It explicitly integrates real-device topology and noise characteristics during both the quantum circuit generation and performance prediction stages. This makes the generated circuits highly compatible with different quantum hardware backends and enhances their fidelity. * Efficient performance estimation with GNNs: HaQGNN employs two specialized Graph Neural Networks (GNNs) for rapid and accurate evaluation of candidate quantum circuits. GNNs-1 predicts the "Probability of Successful Trials (PST)," a metric correlated with circuit fidelity, allowing for the early rejection of low-fidelity circuits affected by hardware noise. GNNs-2 estimates the "Kernel-Target Alignment (KTA)," which is a reliable surrogate metric for classification accuracy. * Noise robustness: By filtering out noisy (low-fidelity circuits early in the pipeline), HaQGNN effectively reduces the impact of gate errors and decoherence, leading to more reliable kernel performance on noisy devices. Here more details: https://lnkd.in/dvGd9sMs #qml #quantum #machinelearning #datascience